A Comparative Analysis of Shape-Based and Zernike Moment Feature Extraction Techniques for Fasteners Recognition Using Neural Network

HADZLI, HASHIM and NOR’AINI, JALIL and NUR DIYANAH, MUSTAFFA KAMAL (2016) A Comparative Analysis of Shape-Based and Zernike Moment Feature Extraction Techniques for Fasteners Recognition Using Neural Network. In: Sixth International Conference On Advances in Computing, Electronics and Electrical Technology - CEET 2016, 26-27 November 2016, Kuala Lumpur, Malaysia.

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Abstract

This paper presents a Comparative Analysis of Shape-based and Zernike moment Feature Extraction Techniques for Fastener Recognition. There a nine features extracted using shape-based technique and 64 moments used in Zernike feature extraction technique. For Zernike moment technique, the 64 moments are divided into 3 groups. The first group is the lower order moments, the second group is the higher order moments and the third group is the combination between the lower order group and the higher order group. The processes taken in the recognition are image acquisition, pre-processing, segmentation, feature extraction, and classification. The segmentation process is carried out by using adaptive filter and the classification process employed artificial neural network. The final result from this experiment is that shape-based technique has a better classification result of about 84.93% correct recognition compared to Zernike moment technique which is about 51.53% (combined group).

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Shape-based technique, Zernike moment,fastener recognition, artificial neural network.
Depositing User: Mr. John Steve
Date Deposited: 20 Mar 2019 11:30
Last Modified: 20 Mar 2019 11:30
URI: http://publications.theired.org/id/eprint/671

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